Representationalism is a dead end

Abstract

Representationalism—the view that scientific modeling is best understood in representational terms—is the received view in contemporary philosophy of science. Contributions to this literature have focused on a number of puzzles concerning the nature of representation and the epistemic role of misrepresentation, without considering whether these puzzles are the product of an inadequate analytical framework. The goal of this paper is to suggest that this possibility should be taken seriously. The argument has two parts, employing the “can’t have” and “don’t need” tactics drawn from philosophy of mind. On the one hand, I propose that representationalism doesn’t work: different ways to flesh out representationalism create a tension between its ontological and epistemological components and thereby undermine the view. On the other hand, I propose that representationalism is not needed in the first place—a position I articulate based on a pragmatic stance on the success of scientific research and on the feasibility of alternative philosophical frameworks. I conclude that representationalism is untenable and unnecessary, a philosophical dead end. A new way of thinking is called for if we are to make progress in our understanding of scientific modeling.

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References

  1. Batterman, R. W., & Rice, C. C. (2014). Minimal model explanations. Philosophy of Science, 81(3), 349–376.

    Article  Google Scholar 

  2. Bokulich, A. (2012). Distinguishing explanatory from nonexplanatory fictions. Philosophy of Science, 79(5), 725–737.

    Article  Google Scholar 

  3. Bokulich, A. (2017). Models and explanation. In Springer handbook of model-based science (pp. 103–118). Springer.

  4. Callender, C., & Cohen, J. (2006). There is no special problem about scientific representation. Theoria Revista de Teoría, Historia y Fundamentos de la Ciencia, 21(1), 67–85.

    Google Scholar 

  5. Chakravartty, A. (2010). Informational versus functional theories of scientific representation. Synthese, 172(2), 197–213.

    Article  Google Scholar 

  6. Contessa, G. (2007). Scientific representation, interpretation, and surrogative reasoning. Philosophy of Science, 74(1), 48–68.

    Article  Google Scholar 

  7. Elgin, C. Z. (2004). True enough. Philosophical Issues, 14(1), 113–131.

    Article  Google Scholar 

  8. Elgin, C. Z. (2017). True enough. Cambridge: MIT Press.

    Book  Google Scholar 

  9. Frigg, R., & Nguyen. J. (2017a). Models and representation. In Springer handbook of model-based science (pp. 49–102). Springer.

  10. Frigg, R., & Nguyen, J. (2017b). Scientific representation is representation-as. In H. K. Chao & J. Reiss (Eds.), Philosophy of science in practice (pp. 149–179). Berlin: Springer.

    Google Scholar 

  11. Gelfert, A. (2017). The ontology of models. In Springer handbook of model-based science (pp. 5–23). Springer.

  12. Giere, R. (2010). An agent-based conception of models and scientific representation. Synthese, 172(2), 269–281.

    Article  Google Scholar 

  13. Giere, R. N. (1988). Explaining science: A cognitive approach. Chicago: University of Chicago Press.

    Book  Google Scholar 

  14. Giere, R. N. (2004). How models are used to represent reality. Philosophy of Science, 71(5), 742–752.

    Article  Google Scholar 

  15. Giere, R. N. (2006). Scientific perspectivism. Chicago: University of Chicago Press.

    Book  Google Scholar 

  16. Godfrey-Smith, P. (2006a). The strategy of model-based science. Biology and Philosophy, 21, 725–740.

    Article  Google Scholar 

  17. Godfrey-Smith, P. (2006b). Theories and models in metaphysics. The Harvard Review of Philosophy, 14(1), 4–19.

    Article  Google Scholar 

  18. Hughes, R. I. (1997). Models and representation. Philosophy of Science, 64, S325–S336.

    Article  Google Scholar 

  19. Hutto, D., & Myin, E. (2013). Radical enactivism: Basic minds without content. Cambridge, MA: MIT Press.

    Google Scholar 

  20. Isaac, A. M. (2013). Modeling without representation. Synthese, 190, 3611–3623.

    Article  Google Scholar 

  21. James, W. (1907). Pragmatism, a new name for some old ways of thinking: Popular lectures on philosophy. Harlow: Longmans, Green and Co.

    Book  Google Scholar 

  22. Kennedy, A. G. (2012). A non representationalist view of model explanation. Studies in History and Philosophy of Science, 43, 326–332.

    Article  Google Scholar 

  23. Knuuttila, T. (2010). Not just underlying structures: Towards a semiotic approach to scientific representation and modeling. In Bergman, M., Paavola, A.P.S., & Rydenfelt, H. (Eds.) Ideas in action: Proceedings of the applying peirce conference (pp. 163–172).

  24. Knuuttila, T. (2011). Modelling and representing: An artefactual approach to model-based representation. Studies in History and Philosophy of Science Part A, 42(2), 262–271.

    Article  Google Scholar 

  25. Lloyd, E. A. (2010). Conrmation and robustness of climate models. Philosophy of Science, 77(5), 971–984.

    Article  Google Scholar 

  26. MacBride, F. (2016). Relations. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy, Winter 2016 edition. Stanford: Metaphysics Research Lab, Stanford University.

    Google Scholar 

  27. Morgan, M., & Morrison, M. (1999). Models as mediators: Perspectives on natural and social science. Ideas in context. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  28. Morrison, M. (2015). Reconstructing reality: Models, mathematics, and simulations. Oxford: Oxford University Press.

    Book  Google Scholar 

  29. Myin, E., & Hutto, D. D. (2015). Rec: Just radical enough. Studies in Logic, Grammar and Rhetoric, 41(1), 61–71.

    Article  Google Scholar 

  30. Parker, W. S. (2011). When climate models agree: The significance of robust model predictions. Philosophy of Science, 78(4), 579–600.

    Article  Google Scholar 

  31. Pincock, C. (2012). Mathematics and scientific representation. Oxford: Oxford University Press.

    Book  Google Scholar 

  32. Potochnik, A. (2015). The diverse aims of science. Studies in History and Philosophy of Science Part A, 53, 71–80.

    Article  Google Scholar 

  33. Potochnik, A. (2017). Idealization and the aims of science. Chicago: The University Chicago Press.

    Book  Google Scholar 

  34. Suarez, M. (2003). Scientific representation: Against similarity and isomorphism. International Studies in the Philosophy of Science, 17(3), 225–244.

    Article  Google Scholar 

  35. Suarez, M. (2004). An inferential conception of scientific representation. Philosophy of Science, 71(5), 767–779.

    Article  Google Scholar 

  36. van Fraassen, B. C. (1980). The scientific image. Oxford: Clarendon Press.

    Book  Google Scholar 

  37. van Fraassen, B. C. (2008). Scientific representation: Paradoxes of perspective. Oxford: Oxford University Press.

    Book  Google Scholar 

  38. Weisberg, M. (2012). Simulation and similarity: Using models to understand the world. Oxford: Oxford University Press.

    Google Scholar 

  39. Wimsatt, W. C. (1987). False models as means to truer theories. In N. Nitecki & A. Hoffman (Eds.), Neutral models in biology (pp. 23–55). Oxford: Oxford University Press.

    Google Scholar 

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Acknowledgements

I have presented ideas related to this paper at various conferences over the past couple of years, and I have benefited from questions and objections raised by more people than I can hope to name. I am grateful for all of these interactions and recognize the crucial role they have played in helping me develop my thinking. Very special thanks go to Angela Potochnik for her extensive and insightful comments on multiple drafts of this paper. My research was supported by a dissertation fellowship from the Charles Phelps Taft Research Center at the University of Cincinnati.

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Correspondence to Guilherme Sanches de Oliveira.

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de Oliveira, G.S. Representationalism is a dead end. Synthese 198, 209–235 (2021). https://doi.org/10.1007/s11229-018-01995-9

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Keywords

  • Scientific modeling
  • Representation
  • Epistemology of science
  • Pragmatism